Continuous Monitoring of Patient Mobility for 18 Months Using Inertial Sensors following Traumatic Knee Injury: A Case Study

Mueller, Arne; Hoefling, Hölger; Nuritdinow, Timur; Holway, Nicholas; Schieker, Matthias; Däumer, Martin; Clay, Ieuan

doi:10.1159/000490919

Cited by 14 publications

(11 citation statements)

References 18 publications

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“…Previous work has shown effective gait speed monitoring in healthy populations by algorithms combining individual detection and parameterization of steps with estimation of gait speed for a given step. Step detection has been achieved with a range of methods including continuous wavelet transform [16,22], whereas gait speed estimation is typically done by using supervised methods such as support vector regression [3]. However, when applied to slow-walking populations, for example, multiple sclerosis (MS), these algorithms produce an observable overestimation of gait speed, particularly in the slowest walkers [6], presumably because of varying relevance of the feature set in patients with pathological gait relative to training sets.…”

Section: Resultsmentioning

confidence: 99%

“…We observed that patient compliance patterns were highly variable, with some patients greatly exceeding the requested wear time and others contributing far less. As previously reported [16], we sought to define a set of minimum thresholds for compliant wear time from which gait behaviors could be stably estimated. This 2-component threshold (hours per day and days around visits) is an attempt to maximize the number of patients included in the analysis and minimize variation because of the sampling of an unrepresentative, too short period of a patient’s daily life.…”

Section: Resultsmentioning

confidence: 99%

“…For both studies, participants were monitored using a waist-worn inertial sensor (actibelt RCT2, Trium Analysis Online, Munich [3,6,16,17]), which recorded acceleration in 3 dimensions at a sampling frequency of 100 Hz (12-bit resolution) and a range of 6g. The devices did not require charging or other interaction from the patients and were otherwise self-managed by the patients.…”

Section: Methodsmentioning

confidence: 99%

“…To allow fair comparisons over time, periods of continuous walking ( bouts ) were grouped according to length, similar to our previous work [16]. To achieve this, contiguous windows were combined into a single bout, provided that the mean vertical frequency ( F x ) did not vary by more than an empirical factor of 1.6-fold between windows.…”

Section: Methodsmentioning

confidence: 99%

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Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial

Mueller¹,

Hoefling²,

Muaremi³

et al. 2019

JMIR Mhealth Uhealth

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BackgroundDigital technologies and advanced analytics have drastically improved our ability to capture and interpret health-relevant data from patients. However, only limited data and results have been published that demonstrate accuracy in target indications, real-world feasibility, or the validity and value of these novel approaches.ObjectiveThis study aimed to establish accuracy, feasibility, and validity of continuous digital monitoring of walking speed in frail, elderly patients with sarcopenia and to create an open source repository of raw, derived, and reference data as a resource for the community.MethodsData described here were collected as a part of 2 clinical studies: an independent, noninterventional validation study and a phase 2b interventional clinical trial in older adults with sarcopenia. In both studies, participants were monitored by using a waist-worn inertial sensor. The cross-sectional, independent validation study collected data at a single site from 26 naturally slow-walking elderly subjects during a parcours course through the clinic, designed to simulate a real-world environment. In the phase 2b interventional clinical trial, 217 patients with sarcopenia were recruited across 32 sites globally, where patients were monitored over 25 weeks, both during and between visits.ResultsWe have demonstrated that our approach can capture in-clinic gait speed in frail slow-walking adults with a residual standard error of 0.08 m per second in the independent validation study and 0.08, 0.09, and 0.07 m per second for the 4 m walk test (4mWT), 6-min walk test (6MWT), and 400 m walk test (400mWT) standard gait speed assessments, respectively, in the interventional clinical trial. We demonstrated the feasibility of our approach by capturing 9668 patient-days of real-world data from 192 patients and 32 sites, as part of the interventional clinical trial. We derived inferred contextual information describing the length of a given walking bout and uncovered positive associations between the short 4mWT gait speed assessment and gait speed in bouts between 5 and 20 steps (correlation of 0.23) and longer 6MWT and 400mWT assessments with bouts of 80 to 640 steps (correlations of 0.48 and 0.59, respectively).ConclusionsThis study showed, for the first time, accurate capture of real-world gait speed in slow-walking older adults with sarcopenia. We demonstrated the feasibility of long-term digital monitoring of mobility in geriatric populations, establishing that sufficient data can be collected to allow robust monitoring of gait behaviors outside the clinic, even in the absence of feedback or incentives. Using inferred context, we demonstrated the ecological validity of in-clinic gait assessments, describing positive associations between in-clinic performance and real-world walking behavior. We make all data available as an open source resource for the community, providing a basis for further study of the relationship between standardized physical performance assessment and real-world behavior and independence.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial

Mueller¹,

Hoefling²,

Muaremi³

et al. 2019

JMIR Mhealth Uhealth

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“…In contrast to gait speed, for which there are few published studies, step detection is well studied with multiple devices on multiple populations [21–26], however different validation protocols, populations and test scenarios make a comparison between existing results challenging, and there is no consensus about standardized setting [21,27,28]. Khan et al and Lipperts et al tested and validated their algorithms in an elderly population[27,28].…”

Section: Discussionmentioning

confidence: 99%

Validity of accelerometry in step detection and gait speed measurement in orthogeriatric patients

et al. 2019

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Background Mobile accelerometry is a powerful and promising option to capture long-term changes in gait in both clinical and real-world scenarios. Increasingly, gait parameters have demonstrated their value as clinical outcome parameters, but validation of these parameters in elderly patients is still limited. Objective The aim of this study was to implement a validation framework appropriate for elderly patients and representative of real-world settings, and to use this framework to test and improve algorithms for mobile accelerometry data in an orthogeriatric population. Methods Twenty elderly subjects wearing a 3D-accelerometer completed a parcours imitating a real-world scenario. High-definition video and mobile reference speed capture served to validate different algorithms. Results Particularly at slow gait speeds, relevant improvements in accuracy have been achieved. Compared to the reference the deviation was less than 1% in step detection and less than 0.05 m/s in gait speed measurements, even for slow walking subjects (< 0.8 m/s). Conclusion With the described setup, algorithms for step and gait speed detection have successfully been validated in an elderly population and demonstrated to have improved performance versus previously published algorithms. These results are promising that long-term and/or real-world measurements are possible with an acceptable accuracy even in elderly frail patients with slow gait speeds.

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The Pivotal Function of Non-human Actors in the Acceptability of the Body Technology, Actibelt®: a Reconstruction Based on Actor-Network-Theory

Scheermesser

2022

Nanoethics

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This paper explores the question of how non-human actors contribute to the acceptability of technologies. Acceptance and acceptability of technologies were examined as network formation and not, as in conventional technology acceptance models, as adoption by individual human actors. Using the approach of translation sociology, the acceptance work necessary for network formation was examined. As a result, the (technical) actibelt®-Actor-Network and five modes of acceptance work by non-human actors and their effects on patients were identified. The different modes of acceptance work show that non-human actors, such as events, meetings, graphs, and socio-technical discourses, are not passive actors in the development of technology, but can enable, hinder, or condition acceptability. Therefore, non-human actors play a central and constitutive role in the translation process by performing acceptance work and contributing to the stabilisation and acceptability of the actibelt®-Actor-Network.

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Continuous Monitoring of Patient Mobility for 18 Months Using Inertial Sensors following Traumatic Knee Injury: A Case Study

Cited by 14 publications

References 18 publications

Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial

Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial

Validity of accelerometry in step detection and gait speed measurement in orthogeriatric patients

The Pivotal Function of Non-human Actors in the Acceptability of the Body Technology, Actibelt®: a Reconstruction Based on Actor-Network-Theory

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